Road　extraction　from　high-resolution　remote　sensing　images　is　a　challenging　but　hot　research　topic　in　the　past　decades.　A　large　number　of　methods　are　invented　to　deal　with　this　problem.　This　article　provides　a　comprehensive　review　of　these　existing　approaches.　We　classified　the　methods　into　heuristic　and　data-driven.　The　heuristic　methods　are　the　mainstream　in　the　early　years,　and　the　data-driven　methods　based　on　deep　learning　have　been　quickly　developed　recently.　With　regard　to　the　heuristic　methods,　the　road　feature　model　is　first　introduced,　then,　the　classic　extraction　methods　are　reviewed　in　two　subcategories:　semiautomatic　and　automatic.　The　principles,　inspirations,　advantages,　and　disadvantages　of　these　methods　are　described.　In　terms　of　the　data-driven　methods,　the　road　extraction　methods　based　on　deep　neural　network,　particularly　those　based　on　patched　convolutional　neural　network,　fully　convolutional　network,　and　generative　adversarial　network　are　reviewed.　We　perform　subjective　comparisons　between　the　methods　inner　each　type.　Furthermore,　the　quantity　performances　achieved　on　the　same　dataset　are　compared　between　the　heuristic　and　data-driven　methods　to　show　the　strengthening　of　the　data-driven　methods.　Finally,　the　conclusion　and　prospects　are　summarized.